The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

BEAST decoding - asymptotic complexity

På svenska

Author

Summary, in English

BEAST is a bidirectional efficient algorithm for searching trees that performs soft-decision maximum-likelihood (ML) decoding of block codes. The decoding complexity of BEAST is significantly reduced compared to the Viterbi algorithm. An analysis of the asymptotic BEAST decoding complexity verifies BEAST's high efficiency compared to other algorithms. The best of the obtained asymptotic upper bounds on the BEAST decoding complexity is better than previously known bounds for ML decoding in a wide range of code rates.

Publishing year

2005

Language

English

Publication/Series

2005 IEEE Information Theory Workshop

Document type

Conference paper

Topic

  • Electrical Engineering, Electronic Engineering, Information Engineering

Keywords

  • maximum likelihood decoding
  • tree searching
  • block codes
  • decision trees
  • computational complexity

Conference name

IEEE IT SOC Information Theory Workshop 2005 on Coding and Complexity

Conference date

2005-08-29 - 2005-09-01

Conference place

Rotorua, New Zealand

Status

Published

ISBN/ISSN/Other

  • ISBN: 0-7803-9480-1